data discovery

"Your organization can’t survive without email. In turn, the data held within email is an invaluable resource for compliance, e-discovery and workflow efficiency purposes. And moving to the cloud involves its own set of challenges around preserving that content.
It’s time to rethink archiving.
Download our E-book, 5 Essentials for Cloud Archiving Success, to learn:
- How to avoid common misconceptions about cloud email archiving
- Ways to enable employee efficiency and productivity through archiving
- Why every organization must be prepared for e-discovery and litigation "

It is imperative that organizations start looking now for smarter solutions to the problems associated with unstructured data. There are many issues related to unstructured data that need to be readily addressed such as storage, discovery, organization, tagging and deduplication. One of the most important issues related to unstructured data is finding and discovering key business insights as quickly as possible, preferably in near real time, to gain a significant competitive advantage.

Companies need capabilities for identifying data assets and relationships, assessing data growth and implementing tiered storage strategies-capabilities that information governance can provide. It is important to classify enterprise data, understand data relationships and define service levels. Database archiving has proven effective in managing continued application data growth especially when it is combined with data discovery.

The application economy has forced businesses to transform. To capture new growth opportunities, enterprises are opening up and sharing select data and applications with developers, partners, mobile devices, the cloud and the Internet of Things (IoT). One of the byproducts of this transformation is the discovery that legacy data has value in the application economy—so much so that new revenue opportunities emerge as this data is used in new ways.

Business decision making is undergoing a data-infused renaissance.
Organizations are tired of the limitations of spreadsheets and
dealing with long IT business intelligence (BI) development cycles
just to gain access to the data they need now. Fortunately, with
the advent of visual analytics and discovery tools (many offered
in the cloud), the journey to data insight is getting simpler and
faster. Rather than trying to divine meaning from a group of
predefined reports or simple static dashboards, visual analytics
helps users gain insights from data more quickly using intuitive data
visualization. Increasingly, visual analytics tools provide easy-touse
data preparation features for better data access. They support
collaboration, mashups, and storytelling.
TDWI Research sees growing interest in applying more modern,
up-to-date tools for working with data.

Making key decisions that improve business performance requires more than simple insights. It takes deep data discovery and a keen problem solving approach to think beyond the obvious. As a business leader, you ought to have access to information most relevant to you that helps you anticipate potential business headwinds and craft strategies which can turn challenges into opportunities finally leading to favorable business outcomes.
WNS DecisionPoint , a one-of-its kind thought leadership platform tracks industry segments served by WNS and presents thought-provoking original perspectives based on rigorous data analysis and custom research studies. Coupling empirical data analysis with practical ideas around the application of analytics, disruptive technologies, next-gen customer experience, process
transformation and business model innovation; we aim to arm you with decision support frameworks based on points of fact.

In the new age of big data, applications are leveraging large farms of powerful servers and extremely fast networks to access petabytes of data served for everything from data analytics to scientific discovery to movie rendering. These new applications demand fast and efficient storage, which legacy solutions are no longer capable of providing.

Business leaders are eager to harness
the power of big data. However, as the
opportunity increases, ensuring that source
information is trustworthy and protected
becomes exponentially more difficult. If not
addressed directly, end users may lose
confidence in the insights generated from
their data—which can result in a failure to
act on opportunities or against threats.
Information integration and governance
must be implemented within big data
applications, providing appropriate
governance and rapid integration from
the start. By automating information
integration and governance and employing
it at the point of data creation, organizations
can boost confidence in big data.
A solid information integration and
governance program must become a
natural part of big data projects, supporting
automated discovery, profiling and
understanding of diverse data sets to
provide context and enable employees
to make informed decisions. It must be
agile to accommodate a wide variety of
data and seamle

An interactive white paper describing how to get smart about insider threat prevention - including how to guard against privileged user breaches, stop data breaches before they take hold, and take advantage of global threat intelligence and third-party collaboration.
Security breaches are all over the news, and it can be easy to think that all the enemies are outside your organization. But the harsh reality is that more than half of all attacks are caused by either malicious insiders or inadvertent actors.1 In other words, the attacks are instigated by people you’d be likely to trust. And the threats can result in significant financial or reputational losses.

Have you adjusted your data retention policies and electronic discovery procedures to comply with the new Federal Rules of Civil Procedure (FRCP)? Learn how email archiving can help you with these electronic discovery requirements.

Waterline Data automates the cataloging of data assets and provides an Amazon.com-like guided shopping approach to data discovery that is intended to take the guesswork out of targeting the right data.

Kylo overcomes common challenges of capturing and processing big data. It lets businesses easily configure and monitor data flows in and through the data lake so users have constant access to high-quality data. It also enhances data profiling while offering self-service and data wrangling capabilities.

Read this Forrester whitepaper to learn more about the critical, yet often overlooked, role that data classification and data discovery can play in reducing your organization’s risk and enhancing security.

A solid information integration and governance program must become a natural part of big data projects, supporting automated discovery, profiling and understanding of diverse data sets to provide context and enable employees to make informed decisions. It must be agile to accommodate a wide variety of data and seamlessly integrate with diverse technologies, from data marts to Apache Hadoop systems. And it must automatically discover, protect and monitor sensitive information as part of big data applications.

In today’s highly distributed, multi-platform world, the data needed to solve any particular decision making need is increasingly likely to be found across a wide variety of sources. As a result, traditional manual approaches requiring prior collection, storage and integration of extensive sets of data in the analyst’s preferred exploration environment are becoming less useful. Data virtualization, which offers transparent access to distributed, diverse data sources, offers a valuable alternative approach in these circumstances.

Business users want the power of analytics—but analytics can only be as good as the data. To perform data discovery and exploration, use analytics to define desired business outcomes, and derive insights to help attain those outcomes, users need good, relevant data. Executives, managers, and other professionals are reaching for self-service technologies so they can be less reliant on IT and move into advanced analytics formerly limited to data scientists and statisticians. However, the biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation.

Read more to learn how IT ensures that they are empowering their users with the tools they need to naturally explore data, ask the
next question, and uncover the insight they need to make informed decisions.

The focus of modern business intelligence has been self-service; pushing data into the hands of end users more quickly with more accessible user interfaces so they can get answers fast and on their own. This has helped alleviate a major BI pain point: centralized, IT-dominated solutions have been too slow and too brittle to serve the business.
What has been masked is a lack of innovation in data modeling. Data modeling is a huge, valuable component of BI that has been largely neglected. In this webinar, we discuss Looker’s novel approach to data modeling and how it powers a data exploration environment with unprecedented depth and agility.
Topics covered include:
• A new architecture beyond direct connect
• Language-based, git-integrated data modeling
• Abstractions that make SQL more powerful and more efficient